import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Tuple     					=		(KDPMaDiscreteScheduler,)
			__a    :					Union[str, Any]     					=		10





			def 			_SCREAMING_SNAKE_CASE     (  self ,  **lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				{
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowerCAmelCase__		)
							return config





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,  [0.0002, 0.002, 0.02]		):
											self.check_over_configs(beta_start=lowerCAmelCase__ ,  beta_end=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config(prediction_type='''v_prediction'''		)
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							scheduler.set_timesteps(self.num_inference_steps		)

							__lowercase					     =				self.dummy_model()
							__lowercase					     =				self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase					     =				sample.to(lowerCAmelCase__		)

							for i, t in enumerate(scheduler.timesteps		):
											__lowercase					     =				scheduler.scale_model_input(lowerCAmelCase__ ,  lowerCAmelCase__		)

											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		)

											__lowercase					     =				scheduler.step(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
											__lowercase					     =				output.prev_sample

							__lowercase					     =				torch.sum(torch.abs(lowerCAmelCase__		)		)
							__lowercase					     =				torch.mean(torch.abs(lowerCAmelCase__		)		)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07		) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10		) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07		) < 1E-2
											assert abs(result_mean.item() - 0.0002		) < 1E-3





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config()
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							scheduler.set_timesteps(self.num_inference_steps		)

							__lowercase					     =				self.dummy_model()
							__lowercase					     =				self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase					     =				sample.to(lowerCAmelCase__		)

							for i, t in enumerate(scheduler.timesteps		):
											__lowercase					     =				scheduler.scale_model_input(lowerCAmelCase__ ,  lowerCAmelCase__		)

											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		)

											__lowercase					     =				scheduler.step(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
											__lowercase					     =				output.prev_sample

							__lowercase					     =				torch.sum(torch.abs(lowerCAmelCase__		)		)
							__lowercase					     =				torch.mean(torch.abs(lowerCAmelCase__		)		)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125		) < 1E-2
											assert abs(result_mean.item() - 0.0266		) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125		) < 1E-2
											assert abs(result_mean.item() - 0.0266		) < 1E-3





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config()
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							scheduler.set_timesteps(self.num_inference_steps ,  device=lowerCAmelCase__		)

							__lowercase					     =				self.dummy_model()
							__lowercase					     =				self.dummy_sample_deter.to(lowerCAmelCase__		) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											__lowercase					     =				scheduler.scale_model_input(lowerCAmelCase__ ,  lowerCAmelCase__		)

											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		)

											__lowercase					     =				scheduler.step(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
											__lowercase					     =				output.prev_sample

							__lowercase					     =				torch.sum(torch.abs(lowerCAmelCase__		)		)
							__lowercase					     =				torch.mean(torch.abs(lowerCAmelCase__		)		)

							if str(lowerCAmelCase__		).startswith('''cpu'''		):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125		) < 1E-2
											assert abs(result_mean.item() - 0.0266		) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125		) < 1E-2
											assert abs(result_mean.item() - 0.0266		) < 1E-3
def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				word.split()

				def justify(lowercase				,     lowercase				,     lowercase				) -> str:
								__lowercase					     =				max_width - width
								__lowercase					     =				len(lowercase				)
								if len(lowercase				) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowercase					     =				words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowercase					     =				spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowercase					     =				(
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowercase				):
																num_spaces_between_words_list[i] += 1
												__lowercase					     =				[]
												for i in range(lowercase				):
																# add the word
																aligned_words_list.append(line[i]				)
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ''' '''				)
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]				)
												# join the aligned words list to form a justified line
												return "".join(lowercase				)

				__lowercase					     =				[]
				__lowercase					     =				[]
				__lowercase					     =				0
				for word in words:
								if width + len(lowercase				) + len(lowercase				) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowercase				)
												width += len(lowercase				)
								else:
												# justify the line and add it to result
												answer.append(justify(lowercase				,     lowercase				,     lowercase				)				)
												# reset new line and new width
												__lowercase     ,	__lowercase					     =				[word], len(lowercase				)
				__lowercase					     =				max_width - width - len(lowercase				)
				answer.append(''' '''.join(lowercase				) + (remaining_spaces + 1) * ''' '''				)
				return answer


if __name__ == "__main__":
		from doctest import testmod

		testmod()